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arXiv 1907.07713eess.IVcs.LGq-bio.QMstat.ML

An AI-Augmented Lesion Detection Framework For Liver Metastases With Model Interpretability

SAS Institute Inc.(SAS研究所公司);SAS Institute B.V.(SAS研究所私人有限公司);Amsterdam University Medical Centers(阿姆斯特丹大学医学中心)

Xin J. Hunt, Ralph Abbey, Ricky Tharrington, Joost Huiskens, Nina Wesdorp

英文摘要:

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related deaths worldwide. Most CRC deaths are the result of progression of metastases. The assessment of metastases is done using the RECIST criterion, which is time consuming and subjective, as clinicians need to manually measure anatomical tumor sizes. AI has many successes in image object detection, but often suffers because the models used are not interpretable, leading to issues in trust and implementation in the clinical setting. We propose a framework for an AI-augmented system in which an interactive AI system assists clinicians in the metastasis assessment. We include model interpretability to give explanations of the reasoning of the underlying models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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